Non-linear correlation models for internal target movement

ABSTRACT

A method and apparatus to track non-linear internal target movement based on movement of an external marker.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/615,008 filed on Oct. 2, 2004.

TECHNICAL FIELD

This invention relates to the field of radiation treatment and, inparticular, to tracking target movement in radiation treatment.

BACKGROUND

Pathological anatomies such as tumors and lesions can be treated with aninvasive procedure, such as surgery, but can be harmful and full ofrisks for the patient. A non-invasive method to treat a pathologicalanatomy (e.g., tumor, lesion, vascular malformation, nerve disorder,etc.) is external beam radiation therapy. In one type of external beamradiation therapy, an external radiation source is used to direct asequence of x-ray beams at a tumor site from multiple angles, with thepatient positioned so the tumor is at the center of rotation (isocenter)of the beam. As the angle of the radiation source changes, every beampasses through the tumor site, but passes through a different area ofhealthy tissue on its way to the tumor. As a result, the cumulativeradiation dose at the tumor is high and the average radiation dose tohealthy tissue is low.

The term “radiotherapy” refers to a procedure in which radiation isapplied to a target region for therapeutic, rather than necrotic,purposes. The amount of radiation utilized in radiotherapy treatmentsessions is typically about an order of magnitude smaller, as comparedto the amount used in a radiosurgery session. Radiotherapy is typicallycharacterized by a low dose per treatment (e.g., 100-200 centiGray(cGy)), short treatment times (e.g., 10 to 30 minutes per treatment) andhyperfractionation (e.g., 30 to 45 days of treatment). For convenience,the term “radiation treatment” is used herein to mean radiosurgeryand/or radiotherapy unless otherwise noted.

In many medical applications, it is useful to accurately track themotion of a moving target region in the human anatomy. For example, inradiosurgery, it is useful to accurately locate and track the motion ofa target region, due to respiratory and other patient motions during thetreatment. Conventional methods and systems have been developed forperforming tracking of a target treatment (e.g. radiosurgical treatment)on an internal target, while measuring and/or compensating for breathingand/or other motions of the patient. For example, U.S. Pat. Nos.6,144,875 and 6,501,981, commonly owned by the assignee of the presentapplication, describe such conventional systems. The SYNCHRONY® system,manufactured by Accuray, Inc., can carry out the methods and systemsdescribed in the above applications.

These conventional methods and systems correlate internal organ movementwith respiration, using a linear model based on respiration position.However, these conventional technologies do not take into accountinternal organ movements along different inspiration and expirationpaths. Although some internal organs may move along one path duringinspiration and along another path during expiration, these conventionaltechnologies do not distinguish these different paths because theyconsider only the position of the internal organ. In particular,conventional technologies use a linear approach to model the organmovement, despite the disparate inspiration and expiration paths of theinternal organ. While the conventional linear modeling may have been animprovement over previous technologies, conventional linear modelingtechnologies are limited in their ability to model multi-path and othernon-linear organ movements.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 illustrates a cross-sectional view of a treatment trackingenvironment.

FIG. 2 is a graphical representation of an exemplary two-dimensionalpath of movement of an internal target during a respiration period.

FIG. 3 is a graphical representation of an exemplary path of movement ofan internal target during a respiration period, as a function of time.

FIG. 4 is a graphical representation of an exemplary set of data pointsassociated with the path of movement shown in FIG. 2.

FIG. 5 is a graphical representation of an exemplary set of data pointsassociated with the path of movement shown in FIG. 3.

FIG. 6 is a graphical representation of an exemplary least square fitlinear correlation model of the path of movement in a first dimension,as a function of movement of an external marker.

FIG. 7 is graphical representation of an exemplary least square fitlinear correlation model of the path of movement in a second dimension,as a function of movement of an external marker.

FIG. 8 is a graphical representation of an exemplary estimated path fora linear correlation model in two dimensions.

FIG. 9 is a graphical representation of an exemplary nonlinearcorrelation model of the path of movement in a first dimension, as afunction of movement of an external marker.

FIG. 10 is a graphical representation of an exemplary nonlinearcorrelation model of the path of movement in a second dimension, as afunction of movement of an external marker.

FIG. 11 is a graphical representation of an exemplary estimated path fora nonlinear correlation model in two dimensions.

FIG. 12 is a graphical representation of an exemplary multi-polycorrelation model of the path of movement in a first dimension, as afunction of movement of an external marker.

FIG. 13 is a graphical representation of an exemplary multi-polycorrelation model of the path of movement in a second dimension, as afunction of movement of an external marker.

FIG. 14 is a graphical representation of an exemplary estimated path fora multi-poly correlation model in two dimensions.

FIG. 15 is a graphical representation of exemplary polynomial matchingapproximations to link the individual polynomial approximations of themulti-poly correlation model of FIG. 14.

FIG. 16 is a graphical representation of an exemplary multi-linearcorrelation model of the path of movement in a first dimension, as afunction of movement of an external marker.

FIG. 17 is a graphical representation of an exemplary multi-linearcorrelation model of the path of movement in a second dimension, as afunction of movement of an external marker.

FIG. 18 is a graphical representation of an exemplary estimated path fora multi-linear correlation model in two dimensions.

FIG. 19 is a graphical representation of exemplary linear matchingapproximations to link the individual linear approximations of themulti-linear correlation model of FIG. 18.

FIG. 20 is a graphical representation of exemplary estimation errors fora plurality of correlation models.

FIG. 21 illustrates one embodiment of a modeling method.

FIG. 22 illustrates one embodiment of a selection method.

FIG. 23 illustrates one embodiment of a tracking method.

FIG. 24 illustrates one embodiment of a treatment system that may beused to perform radiation treatment in which embodiments of the presentinvention may be implemented.

FIG. 25 is a schematic block diagram illustrating one embodiment of atreatment delivery system.

FIG. 26 illustrates a three-dimensional perspective view of a radiationtreatment process.

DETAILED DESCRIPTION

The following description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth, inorder to provide a good understanding of several embodiments of thepresent invention. It will be apparent to one skilled in the art,however, that at least some embodiments of the present invention may bepracticed without these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present invention. Thus, the specific details set forth are merelyexemplary. Particular implementations may vary from these exemplarydetails and still be contemplated to be within the spirit and scope ofthe present invention.

Embodiments of the present invention include various operations, whichwill be described below. These operations may be performed by hardwarecomponents, software, firmware, or a combination thereof. As usedherein, the term “coupled to” may mean coupled directly or indirectlythrough one or more intervening components. Any of the signals providedover various buses described herein may be time multiplexed with othersignals and provided over one or more common buses. Additionally, theinterconnection between circuit components or blocks may be shown asbuses or as single signal lines. Each of the buses may alternatively beone or more single signal lines and each of the single signal lines mayalternatively be buses.

Certain embodiments may be implemented as a computer program productwhich may include instructions stored on a machine-readable medium.These instructions may be used to program a general-purpose orspecial-purpose processor to perform the described operations. Amachine-readable medium includes any mechanism for storing ortransmitting information in a form (e.g., software, processingapplication) readable by a machine (e.g., a computer). Themachine-readable medium may include, but is not limited to, magneticstorage media (e.g., floppy diskette); optical storage media (e.g.,CD-ROM); magneto-optical storage media; read-only memory (ROM);random-access memory (RAM); erasable programmable memory (e.g., EPROMand EEPROM); flash memory; electrical, optical, acoustical, or otherform of propagated signal (e.g., carrier waves, infrared signals,digital signals, etc.); or another type of media suitable for storingelectronic instructions.

Additionally, some embodiments may be practiced in distributed computingenvironments where the machine-readable medium is stored on and/orexecuted by more than one computer system. In addition, the informationtransferred between computer systems may either be pulled or pushedacross the communication medium connecting the computer systems such asin a remote diagnosis or monitoring system. In remote diagnosis ormonitoring, a user may diagnose or monitor a patient despite theexistence of a physical separation between the user and the patient. Inaddition, the treatment delivery system may be remote from the treatmentplanning system.

Embodiments of a method and apparatus are described to track internalobject movement based on movement of an external marker. In oneembodiment, a method and system are presented to identify thecorrelation between movement(s) of a target such as an internal organand respiration (or other motion such as heartbeat) of a patient. Thesemovements may include linear movements, non-linear movements, andasymmetric movements. In one embodiment, the method and system mayfacilitate modeling movement paths of a target that moves alongdifferent paths during inspiration and expiration, respectively. Oneembodiment of the correlation model employs a curvilinear, rather thanrectilinear, model to correlate the internal position of the target torespiration.

The method and system may consider position, speed, and/or direction ofrespiration or the internal object to develop one or more correlationmodels. The method and system also may use data points in time for whichthe position of the target is known. Respiration may be monitored inparallel with the monitoring of the target position. Information aboutthe position and the speed/direction of respiration may be obtained atthe time of interest. Once established, a correlation model may be usedalong with a respiration monitoring system to locate and track theinternal movement of a target such as an organ, region, lesion, tumor,and so forth.

FIG. 1 illustrates a cross-sectional view of a treatment trackingenvironment. The treatment tracking environment depicts correspondingmovements of an internal target 10 within a patient, a linearaccelerator (LINAC) 20, and an external marker 25. The illustratedtreatment tracking environment is representative of a patient chestregion, for example, or another region of a patient in which an internalorgan might move during the respiratory cycle of the patient. Ingeneral, the respiratory cycle of a patient will be described in termsof an inspiration interval and an expiration interval, although otherdesignations and/or delineations may be used to describe a respiratorycycle.

In one embodiment, the LINAC 20 moves in one or more dimensions toposition and orient itself to deliver a radiation beam 12 to the target10. Although substantially parallel radiation beams 12 are depicted, theLINAC 20 may move around the patient in multiple dimensions to projectradiation beams 12 from several different locations and angles. TheLINAC 20 tracks the movement of the target 10 as the patient breathes,for example. One or more external markers 25 are secured to the exterior30 of the patient in order to monitor the patient's breathing cycle. Inone embodiment, the external marker 25 may be a device such as a lightsource or a metal button attached to a vest worn by the patient.Alternatively, the external marker 25 may be attached to the patient'sclothes or skin in another manner.

As the patient breathes, a tracking sensor 32 tracks the location of theexternal marker 25. For example, the tracking sensor may track upwardmovement of the external marker 25 during the inspiration interval anddownward movement of the external marker 25 during the expirationinterval. The relative position of the external marker 25 is correlatedwith the location of the target organ 10, as described below, so thatthe LINAC 20 may move relative to the location of the external marker 25and the correlated location of the target organ 10. In anotherembodiment, other types of external or internal markers may be usedinstead of or in addition to the illustrated external marker 25.

As one example, the depicted target 10 is shown in four positionsdesignated as D₀, D₃, D₅, and D₇. The first position, D₀, may correspondto approximately the beginning of the inspiration interval. The secondposition, D₃, may correspond to a time during the inspiration interval.The third position, D₅, may correspond to approximately the end of theinspiration interval and the beginning of the expiration interval. Thefourth position, D₇, may correspond to a time during the expirationinterval. Additional positions of the target 10 on the path of movementare graphically shown and described in more detail with reference to thefollowing figures. As the patient breathes, the target 10 may move alonga path within the patient's body. In one embodiment, the path of thetarget 10 is asymmetric in that the target 10 travels along differentpaths during the inspiration and expiration intervals. In anotherembodiment, the path of the target 10 is at least partially non-linear.The path of the target 10 may be influenced by the size and shape of thetarget 10, organs and tissues surrounding the target 10, the depth orshallowness of the patient's breathing, and so forth.

Similarly, the external marker 25 is shown in a first position, D₀, asecond position, D₃, a third position, D₅, and a fourth position, D₇,which correspond to the positions of the target 10. By correlating thepositions of the external marker 25 to the target 10, the position ofthe target 10 may be derived from the position of the external marker 25even though the external marker 25 may travel in a direction or along apath that is substantially different from the path and direction of thetarget object 10. The LINAC 20 is also shown in a first position, D₀, asecond position, D₃, a third position, D₅, and a fourth position, D₇,which also correspond to the positions of the target 10. In this way,the movements of the LINAC 20 may be substantially synchronized to themovements of the target 10 as the position of the target 10 iscorrelated to the sensed position of the external marker 25.

FIG. 2 is a graphical representation 35 of an exemplary two-dimensionalpath of movement of an internal target 10 during a respiration period.The horizontal axis represents displacement (e.g., in millimeters) ofthe target 10 in a first dimension (x). The vertical axis representsdisplacement (e.g., in millimeters) of the target 10 in a seconddimension (z). The target 10 may similarly move in a third dimension(y). As shown in the graph 35, the path of movement of the target 10 isnon-linear. Additionally, the path of movement is different during aninspiration period and an expiration period. As an example, theinspiration path may correspond to the upper portion of the graph 35between zero and twenty-five in the x direction, with zero being astarting reference position, D₀, and twenty-five being the maximumdisplacement position, D₅, at the moment between inspiration andexpiration. The corresponding expiration period may be the lower portionof the graph 35 between D₅ and D₀. In the depicted embodiment, thedisplacement position D₃ is on the inspiration path roughly between D₀and D₅. Similarly, the displacement position D₇ is on the expirationpath roughly between D₅ and D₀. These displacement points are shown withadditional displacement points in FIG. 4.

FIG. 3 is a graphical representation 40 of an exemplary path of movementof an internal target 10 during a respiration period, as a function oftime. The graph 40 shows the displacement (e.g., in millimeters) of thetarget 10 over time (e.g., in seconds) in the x direction (dashed line)and in the z direction (solid line). The graph 40 also shows thedisplacement (in millimeters) of, for example, an external marker 10 toidentify the respiration period (dashed line). In the depictedembodiment, the external marker 25 is maximally displaced (approximately30 mm) more than the target organ 10 in the x direction (approximately25 mm) or in the z direction (approximately 8 mm). However, the maximumdisplacement of the target organ 10 in the various directions does notnecessarily align with the maximum displacement of the external marker25 associated with the respiratory period. Additionally, the maximumdisplacement of the target organ 10 in the one direction does notnecessarily align with the maximum displacement in another direction.For example, the maximum displacement of the external marker 25 occur atapproximately 1.75 s, while the maximum displacement of the internalorgan 10 in the x and z directions may occur at approximately 2.0 and1.5 seconds, respectively. These misalignments may be present in boththe inspiration and expiration paths.

FIG. 4 is a graphical representation 45 of an exemplary set of datapoints D₀-D₉ associated with the path of movement shown in FIG. 2. Inparticular, the data points D₀-D₉ are superimposed on the path ofmovement of the target 10. The data points D₀-D₉ correspond to variouspoints in time during the respiration period. In the illustratedembodiment, one data point data point D₀ designates the initialreference location of the target 10 prior to the inspiration interval.Four data points D₁-D₄ designate the movement of the target 10 duringthe inspiration interval. The data point D₅ designates the momentbetween the inspiration and expiration intervals. The data points D₆-D₉designate the movement of the target 10 during the expiration interval.The following table provides approximate coordinates for each of thedata points D₀-D₉. Similar coordinates may be provided for thedisplacement of the external marker 25 or the displacement of the target10 in another direction.

TABLE 1 Data Point Coordinates. Data Point (x, z) (mm) D₀ (0, 1) D₁ (2,3) D₂ (8, 5) D₃ (14, 7)  D₄ (24, 8)  D₅ (25, 7)  D₆ (23, 5)  D₇ (16, 2) D₈ (8, 0) D₉ (1, 0)

FIG. 5 is a graphical representation 50 of the exemplary set of datapoints D₀-D₉ associated with the paths of movement shown in FIG. 3. Thedata points D₀-D₉ are represented by vertical lines superimposed on thepath of movement of the target 10 and the external marker 25. Thefollowing table provides approximate times corresponding to each of thedata points D₀-D_(g), as well as approximate displacement values, r, forthe external marker 25.

TABLE 2 Data Point Times. Data Point Time (s) r (mm) D₀ 0.0 1 D₁ 0.4 6D₂ 0.8 16 D₃ 1.1 22 D₄ 17 30 D₅ 2.4 28 D₆ 2.8 23 D₇ 3.2 14 D₈ 3.7 5 D₉4.0 0

FIG. 6 is a graphical representation 55 of an exemplary least square fitlinear correlation model of the path of movement in a first dimension,as a function of the displacement, r, of an external marker 10. Inparticular, the graph 55 shows the (r,x) coordinates from the datapoints D₀-D₉ above and superimposes a linear correlation model (dashedline). The following table provides approximate (r,x) coordinatescorresponding to each of the data points D₀-D₉.

TABLE 3 Data Point Coordinates. Data Point (r, x) (mm) D₀ (1, 0) D₁ (6,2) D₂ (16, 8)  D₃ (22, 14) D₄ (30, 24) D₅ (28, 25) D₆ (23, 23) D₇ (14,16) D₈ (5, 8) D₉ (0, 1)

The linear correlation model may be used to estimate the x displacementof the target 10 based on the respiration displacement, r, measured bythe external marker 25. The following equation is exemplary of a linearcorrelation model that may be employed in conventional linear modelingsystems:

$\begin{Bmatrix}x_{organ} \\y_{organ} \\z_{organ}\end{Bmatrix} = {{\begin{Bmatrix}A_{x} \\A_{y} \\A_{z}\end{Bmatrix}r} + \begin{Bmatrix}B_{x} \\B_{y} \\B_{z}\end{Bmatrix}}$which may be written in a more compact form as follows:x=ar+b

FIG. 7 is a graphical representation 60 of an exemplary least square fitlinear correlation model of the path of movement in a second dimension,as a function of the displacement, r, of an external marker 10. Inparticular, the graph 60 shows the (r,z) coordinates from the datapoints D₀-D₉ above and superimposes a linear correlation model (dashedline). The following table provides approximate (r,z) coordinatescorresponding to each of the data points D₀-D₉. The linear correlationmodel may be used to estimate the z displacement of the target 10 basedon the respiration displacement, r, measured by the external marker 25.

TABLE 4 Data Point Coordinates. Data Point (r, z) (mm) D₀  (1, 1) D₁ (6, 3) D₂ (16, 5) D₃ (22, 7) D₄ (30, 8) D₅ (28, 7) D₆ (23, 5) D₇ (14,2) D₈  (5, 0) D₉  (0, 0)

FIG. 8 is a graphical representation 65 of an exemplary estimated pathfor a linear correlation model. The graph 65 superimposes the linearcorrelation model for the x and z directions on the path of movement,shown in FIG. 2, of the target 10. While the linear correlation model isfairly accurate at about (x,z)=(2,0) and (x,z)=(23,8), the linearcorrelation model has relatively large estimation errors for all of theother coordinates along the path of movement. The estimation errorcorresponding to the x direction may be determined by the verticaldifference (e.g., in millimeters) between the linear correlation modeland either the inspiration path (e.g., upper portion) or the expirationpath (e.g., lower portion). Similarly, the estimation error for the zdirection may be determined by the horizontal difference (e.g., inmillimeters) between the linear correlation model and either theinspiration path (e.g., upper portion) or the expiration path (e.g.,lower portion). The estimation errors are shown and described in moredetail with reference to FIG. 20.

FIG. 9 is a graphical representation 70 of an exemplary nonlinearcorrelation model of the path of movement in a first dimension, as afunction of the displacement, r, of an external marker 10. Inparticular, the graph 70 shows the (r,x) coordinates from the datapoints D₀-D₉ above and superimposes a nonlinear correlation model(dashed line). The approximate (r,x) coordinates corresponding to eachof the data points D₀-D₉ is provided in Table 3 above.

The nonlinear correlation model may be used to estimate the xdisplacement of the target 10 based on the respiration displacement, r,measured by the external marker 25. The following equation is exemplaryof a nonlinear correlation model:x=f(r)where f(r) describes the curve and may be selected depending on theshape of the path of movement of the target 10. In a more particularembodiment, a third order polynomial may be selected as an example ofthe vector function f(r) of the equation above. In one embodiment, theresulting polynomial curve may be described according to the followingequation:

$x = {\sum\limits_{n = 0}^{3}{a_{n}r^{n}}}$

In another embodiment, the speed of the respiratory motion (i.e., thederivative of the respiration displacement, r) may be used to build anonlinear correlation model, as illustrated, that more closelyapproximates the organ path. For example, using the speed of theexternal marker 25 may be useful in cases in which the target 10 takesdifferent paths during the inspiration and expiration intervals,respectively, of the respiration period. In other embodiments, thedisplacement and/or speed of other motions, other than respiration, maybe used in addition to or instead of the respiration. One example of anequation that takes into account both displacement, r, and speed, {dotover (r)}, as a second independent variable, is as follows:x=f(r,{dot over (r)})

FIG. 10 is a graphical representation 75 of an exemplary nonlinearcorrelation model of the path of movement in a second dimension, as afunction of the displacement, r, of an external marker 10. Inparticular, the graph 75 shows the (r,z) coordinates from the datapoints D₀-D₉ above and superimposes a nonlinear correlation model(dashed line). The approximate (r,z) coordinates corresponding to eachof the data points D₀-D₉ are provided in Table 4 above.

FIG. 11 is a graphical representation 80 of an exemplary estimated pathfor a nonlinear correlation model. The graph 80 superimposes thenonlinear correlation model for the x and z directions on the path ofmovement, shown in FIG. 2, of the target 10. While the nonlinearcorrelation model is fairly accurate at about (x,z)=(2,0) and(x,z)=(23,8), the nonlinear correlation model has relatively largeestimation errors for all of the other coordinates along the path ofmovement. Nevertheless, the nonlinear correlation model has anestimation error that may be smaller than the estimation error of thelinear correlation model. The estimation errors are shown and describedin more detail with reference to FIG. 20.

FIG. 12 is a graphical representation 85 of an exemplary multi-polycorrelation model of the path of movement in a first dimension, as afunction of the displacement, r, of an external marker 10. Inparticular, the graph 85 shows the (r,x) coordinates from the datapoints D₀-D₉ above and superimposes a multi-poly correlation model(dashed line). The multi-poly correlation model also may be referred toherein as a curvilinear correlation model or, more generally, anonlinear correlation model. The approximate (r,x) coordinatescorresponding to each of the data points D₀-D₉ is provided in Table 3above.

The multi-poly correlation model may be used to estimate the xdisplacement of the target 10 based on the speed, {dot over (r)}, andthe direction of motion (i.e., the positive or negative sign of {dotover (r)}) of the external marker 25. In one embodiment, the directionalindicators may be used to split the path of movement of the target 10into two separate curvilinear paths. The directional indicators also maybe used to distinguish the data points D₁-D₄ corresponding to theinspiration interval from the data points D₆-D₉ corresponding to theexpiration interval. In another embodiment, this approach may beimplemented with a third order polynomial, as described above, and themulti-poly correlation model may be described by the following equation:

$x = \left\{ \begin{matrix}{\sum\limits_{n = 0}^{3}{a_{n}^{+}r^{n}}} & {\overset{.}{r} \geq 0} \\{\sum\limits_{n = 0}^{3}{a_{n}^{-}r^{n}}} & {\overset{.}{r} < 0}\end{matrix} \right.$

In one embodiment, the foregoing equation essentially separates the datapoints into two separate groups according to their respective directionof motion of each data point. In particular, data points whose directionis positive (according to a predetermined sign convention) may be placedin a first data set and data points whose direction is negative may beplaced in a second data set. The data sets may correspond to theinspiration and expiration intervals. However, in another embodiment,the data sets for each of the polynomial approximations may overlap. Forexample, data points that have a relatively small directional value maybe placed in more than one data set, regardless of sign. As an example,the data points D₀, D₄, D₅, and D₉ may be placed in each of two datasets. Accordingly, the foregoing equations may be modified to accountfor these overlapping data sets. The outputs of multiple polynomials maybe averaged for the data points that belong to more than one data set.In another embodiment, more than two polynomial approximations may beused to approximate the movement of the target 10.

FIG. 13 is a graphical representation 90 of an exemplary multi-polycorrelation model of the path of movement in a second dimension, as afunction of the displacement, r, of an external marker 10. Inparticular, the graph 90 shows the (r,z) coordinates from the datapoints D₀-D₉ above and superimposes a multi-poly correlation model(dashed line). The approximate (r,z) coordinates corresponding to eachof the data points D₀-D₉ are provided in Table 4 above.

FIG. 14 is a graphical representation 95 of an exemplary estimated pathfor a multi-poly correlation model. The graph 95 superimposes themulti-poly correlation model for the x and z directions on the path ofmovement, shown in FIG. 2, of the target 10. In comparison to the linearcorrelation model and third order nonlinear correlation model describedabove, the multi-poly correlation model is much more accurate for most,if not all, of the coordinates along the path of movement of the target10.

The illustrated multi-poly correlation model includes two polynomialapproximations. However, other embodiments may include more than twopolynomial approximations. In another embodiment, the multi-polycorrelation model also may include one or more linear approximations toapproximate a portion of the path of movement.

FIG. 15 is a graphical representation of exemplary polynomial matchingapproximations to link the individual polynomial approximations of themulti-poly correlation model of FIG. 14. In one embodiment, the matchingapproximations may link an inspiration polynomial approximation and anexpiration polynomial approximation at about the region corresponding tothe moments between the inspiration and expiration periods (near x=0 andx=25). Although two matching approximations are shown at each linkingregion (near x=0 and x=25), other embodiments may implement fewer ormore matching approximations. In another embodiment, the matchingapproximations may include polynomial approximations, linearapproximations, or a combination thereof.

FIG. 16 is a graphical representation 100 of an exemplary multi-linearcorrelation model of the path of movement in a first dimension, as afunction of the displacement, r, of an external marker 10. Themulti-linear correlation model also may be referred to herein, moregenerally, as a nonlinear correlation model. In particular, the graph100 shows the (r,x) coordinates from the data points D₀-D₉ above andsuperimposes a multi-linear correlation model (dashed line). Theapproximate (r,x) coordinates corresponding to each of the data pointsD₀-D₉ is provided in Table 3 above.

The multi-linear correlation model may be used to estimate the xdisplacement of the target 10 based on the speed, {dot over (r)}, andthe direction of motion (i.e., the positive or negative sign of {dotover (r)}) of the external marker 25, as described above. However,multiple linear approximations may be used instead of polynomialapproximations. Linear or polynomial matching approximations also may beused to link the multiple linear approximations to one another, asdescribed above.

FIG. 17 is a graphical representation 105 of an exemplary multi-linearcorrelation model of the path of movement in a second dimension, as afunction of the displacement, r, of an external marker 10. Inparticular, the graph 105 shows the (r,z) coordinates from the datapoints D₀-D₉ above and superimposes a multi-linear correlation model(dashed line). The approximate (r,z) coordinates corresponding to eachof the data points D₀-D₉ are provided in Table 4 above.

FIG. 18 is a graphical representation 110 of an exemplary estimated pathfor a multi-linear correlation model. The graph 110 superimposes themulti-linear correlation model for the x and z directions on the path ofmovement, shown in FIG. 2, of the target 10. In comparison to the linearcorrelation model and third order nonlinear correlation model describedabove, the multi-linear correlation model is much more accurate formost, if not all, of the coordinates along the path of movement of thetarget 10. The multi-linear correlation model may or may not be moreaccurate than the multi-poly correlation model described above. Incertain embodiments, the multi-linear correlation model includes two ormore linear approximations. In another embodiment, the multi-linearcorrelation model also may include one or more polynomial approximationsto approximate a portion of the path of movement.

FIG. 19 is a graphical representation of exemplary linear matchingapproximations to link the individual linear approximations of themulti-linear correlation model of FIG. 18. In one embodiment, thematching approximations may link the two or more linear and/orpolynomial approximations together. For example, the matchingapproximations may link an inspiration linear approximation and anexpiration linear approximation at about the region corresponding to themoments between the inspiration and expiration periods (near x=0 andx=25). Although four matching approximations are shown at each linkingregion (near x=0 and x=25), other embodiments may implement fewer ormore matching approximations. Furthermore, other embodiments mayimplement an odd number of matching approximations. In anotherembodiment, the matching approximations may include polynomialapproximations, linear approximations, or a combination thereof.

FIG. 20 is a graphical representation 115 of exemplary estimation errorsfor a plurality of correlation models. In particular, the graph 115illustrates estimation errors for a linear correlation model (stars), anonlinear polynomial correlation model (squares), a multi-polycorrelation model (diamonds), and a multi-linear correlation model(circles) at data points D₁-D₄ and D₆-D₉ (data point D₀ might fallbetween data points D₉ and D₀; data point D₅ might fall between datapoints D₄ and D₆). The illustrated estimation errors for the nonlinearpolynomial and multi-poly correlations models are specifically based onthird order polynomial approximations, but may be based on higher orderapproximations in other embodiments.

The graph 115 shows that the polynomial correlation model has anestimation error that is slightly lower, for the most part, than theestimation error associated with the linear correlation model. However,the estimation error associated with the multi-linear and multi-polycorrelation models are significantly lower than the estimation errorsfor the linear and nonlinear polynomial correlation models. In oneembodiment, the estimation error for the multi-poly correlation model isless than one mm, compared to four or five mm for the linear andnonlinear polynomial correlation models. In another embodiment, theestimation error for the multi-poly correlation model may beapproximately one-tenth of the estimation error for the linear andnonlinear polynomial correlation models. The estimation error for themulti-linear correlation model may be slighting higher than theestimation error for the multi-poly correlation model, but significantlylower compared to the estimation errors for the linear and nonlinearpolynomial correlation models.

FIG. 21 illustrates one embodiment of a modeling method 150. In oneembodiment, the depicted modeling method 150 may be implemented inhardware, software, and/or firmware on a treatment planning system, suchas the treatment planning system 530 of FIG. 24. Although the modelingmethod 150 is described in terms of the treatment planning system 530,embodiments the modeling method 150 may be implemented on another systemor independent of the treatment planning system 530.

The illustrated modeling method 150 begins and the treatment planningsystem 530 acquires 155 an initial data set of locations of an externalmarker 25. The treatment planning system 530 also acquires 155 one ormore images of the target 10. The location of the target 10 may bederived from these images. The position of the target 10 also may bedetermined relative to the location of the external marker 25.

The treatment planning system 530 subsequently uses the data set andimages to develop 160 a linear correlation model, as described abovewith reference to FIGS. 7-9. The treatment planning system 530 also usesthe data set and images to develop 165 a nonlinear polynomialcorrelation model, as described above with reference to FIG. 9-11. Thetreatment planning system 530 also uses the data set and images todevelop 170 a multi-poly correlation model, as described above withreference to FIG. 12-14. The treatment planning system 530 also uses thedata set and images to develop 175 a multi-linear correlation model, asdescribed above with reference to FIG. 16-18. Although the illustratedmodeling method 150 develops several types of correlation models, otherembodiments of the modeling method 150 may develop fewer or morecorrelation models, including some or all of the correlation modelsdescribed herein.

The treatment planning system 530 maintains these correlation modelsand, in certain embodiments, monitors 180 for or acquires new dataand/or images. When new data or images are received, the treatmentplanning system updates 185 the data set and or the images and mayiteratively develop new models based on the new information. In thisway, the modeling method 150 may maintain the correlation models inreal-time.

FIG. 22 illustrates one embodiment of a selection method 200. In oneembodiment, the depicted selection method 200 may be implemented inhardware, software, and/or firmware on a treatment planning system, suchas the treatment planning system 530 of FIG. 24. Although the selectionmethod 200 is described in terms of the treatment planning system 530,embodiments the selection method 200 may be implemented on anothersystem or independent of the treatment planning system 530.

The illustrated selection method 200 begins and the treatment planningsystem 530 determines 205 if the displacement of the external marker 25is within the boundaries of the various correlation models. For example,many of the correlation models described above have a displacement rangebetween approximately zero and 30 mm. A patient may potentially inhaleor exhale in a way that moves the external marker 25 outside of acorrelation model range. If the displacement of the external marker 25is not within the range of the correlation models, then the treatmentplanning system 530 may select 210 the linear correlation model andextrapolate outside of the model boundaries. Alternatively, thetreatment planning system 530 may select another correlation model suchas the multi-linear correlation model and determine an estimatedlocation of the target 10 from the selected correlation model.

If the displacement of the external marker 25 is within the range of thecorrelation models, then the treatment planning system 530 may generate215 an estimation error for the linear correlation model. In generating215 the estimation error, the treatment planning system 530 may use apredetermined linear weighting factor that increases or decreases theestimation error. In this way, the treatment planning system 530 maygive priority to certain types of correlation models based on a userselection, a type of treatment, or another treatment planning ordelivery factor. In another embodiment, the treatment planning system530 may have previously calculated an estimation error for the linearcorrelation model.

The treatment planning system 530 also generates 220 an estimation errorfor the nonlinear polynomial model. The treatment planning system 530may use a predetermined polynomial weighting factor in generating 220the estimation error. The treatment planning system 530 also generates225 an estimation error for the multi-poly model. The treatment planningsystem 530 may use a predetermined multi-poly weighting factor ingenerating 225 the estimation error. The treatment planning system 530also generates 230 an estimation error for the multi-linear model. Thetreatment planning system 530 may use a predetermined multi-linearweighting factor in generating 230 the estimation error. In anotherembodiment, one or more of the estimation errors may be generatedpreviously.

The treatment planning system 530 subsequently compares 235 all of theestimation errors 235 for all of the correlation models. Alternatively,the treatment planning system 530 may compare some, but not all of theof the estimation errors. In one embodiment, the treatment planningsystem 530 compares the estimation errors only at the current orlocation of the external marker 25. In another embodiment, the treatmentplanning system 530 may generate a composite estimation error based ontwo or more marker locations for each of the correlation models.

The treatment planning system 530 then identifies 240 the bestestimation error according to any rules, weighting factors, or otherpredetermined considerations, and selects 245 the identified correlationmodel for use. In one embodiment, the treatment planning system 530 mayimplement the modeling method 150 and/or the selection method 200 eachtime a new data point or image is received. In another embodiment, thetreatment planning system 530 may implement the modeling method 150and/or the selection method 200 less frequently or independently of oneanother. For example, the treatment planning system 530 may develop newcorrelation models once for every respiratory period and evaluate modelselection multiple times during each respiratory period. In certainembodiments, the treatment planning system 530 may use linear orpolynomial matching approximations to switch from one correlation modelto another. In this way, the correlation models may be blended and thetransition from one correlation model to another may be smooth andimperceptible to a patient.

FIG. 23 illustrates one embodiment of a tracking method 250. In oneembodiment, the tracking method 250 may be implemented in conjunctionwith a treatment system such as the treatment system 500 of FIG. 24.Furthermore, the depicted tracking method 250 may be implemented inhardware, software, and/or firmware on a treatment system 500. Althoughthe tracking method 250 is described in terms of the treatment system500, embodiments the tracking method 250 may be implemented on anothersystem or independent of the treatment system 500.

The illustrated tracking method 250 begins and the treatment system 500performs calibration 255 to initialize model development and selection.In one embodiment, such calibration includes performing the modelingmethod 150 and the selection method 200 prior to treatment delivery. Inanother embodiment, the modeling method 150 and/or the selection method200 may be performed multiple times to establish historical data.

After the tracking system 500 is calibrated, the tracking system 500derives 260 a target position of the target 10 based on the selectedcorrelation model. As described above, the target location of the target10 may be related to the known position of the external marker 25 andderived from one of the correlation models. The tracking system 265subsequently sends 265 a position signal indicating the target positionto a beam generator controller. In one embodiment, the treatment system500 delivers the position signal to a treatment delivery system such asthe treatment delivery system 550 of FIG. 24. The treatment deliverysystem 550 then moves 270 and orients the beam generator such as theradiation source 552 of FIG. 24. The treatment delivery system 550 andradiation source 552 are described in more detail below.

The treatment planning system 530 continues to acquire 275 new datapoints of the external marker 25 and new images of the target 10. In oneembodiment, the treatment planning system 530 may repeatedly developmodels according to the modeling method 150 and select a model accordingto the selection method 200, as described above. In another embodiment,the treatment planning system 530 may select and use one model to derivemultiple target positions. The tracking method 250 may continue in thismanner of developing one or more models, selecting a model, anddelivering treatment according to the selected model for the duration ofa treatment session.

FIG. 24 illustrates one embodiment of a treatment system 500 that may beused to perform radiation treatment in which features of the presentinvention may be implemented. The depicted treatment system 500 includesa diagnostic imaging system 510, a treatment planning system 530, and atreatment delivery system 550. In other embodiments, the treatmentsystem 500 may include fewer or more component systems.

The diagnostic imaging system 510 is representative of any systemcapable of producing medical diagnostic images of a volume of interest(VOI) in a patient, which images may be used for subsequent medicaldiagnosis, treatment planning, and/or treatment delivery. For example,the diagnostic imaging system 510 may be a computed tomography (CT)system, a magnetic resonance imaging (MRI) system, a positron emissiontomography (PET) system, an ultrasound system, or another similarimaging system. For ease of discussion, any specific references hereinto a particular imaging system such as a CT x-ray imaging system isrepresentative of the diagnostic imaging system 510, generally, and doesnot preclude other imaging modalities, unless noted otherwise.

The illustrated diagnostic imaging system 510 includes an imaging source512, an imaging detector 514, and a digital processing system 516. Theimaging source 512, imaging detector 514, and digital processing system516 are coupled to one another via a communication channel 518 such as abus. In one embodiment, the imaging source 512 generates an imaging beam(e.g., x-rays, ultrasonic waves, radio frequency waves, etc.) and theimaging detector 514 detects and receives the imaging beam.Alternatively, the imaging detector 514 may detect and receive asecondary imaging beam or an emission stimulated by the imaging beamfrom the imaging source (e.g., in an MRI or PET scan). In oneembodiment, the diagnostic imaging system 510 may include two or morediagnostic imaging sources 512 and two or more corresponding imagingdetectors 514. For example, two x-ray sources 512 may be disposed arounda patient to be imaged, fixed at an angular separation from each other(e.g., 90 degrees, 45 degrees, etc.) and aimed through the patienttoward corresponding imaging detectors 514, which may be diametricallyopposed to the imaging sources 514. A single large imaging detector 514,or multiple imaging detectors 514, also may be illuminated by each x-rayimaging source 514. Alternatively, other numbers and configurations ofimaging sources 512 and imaging detectors 514 may be used.

The imaging source 512 and the imaging detector 514 are coupled to thedigital processing system 516 to control the imaging operations andprocess image data within the diagnostic imaging system 510. In oneembodiment, the digital processing system 516 may communicate with theimaging source 512 and the imaging detector 514. Embodiments of thedigital processing system 516 may include one or more general-purposeprocessors (e.g., a microprocessor), special purpose processors such asa digital signal processor (DSP), or other type of devices such as acontroller or field programmable gate array (FPGA). The digitalprocessing system 516 also may include other components (not shown) suchas memory, storage devices, network adapters, and the like. In oneembodiment, the digital processing system 516 generates digitaldiagnostic images in a standard format such as the Digital Imaging andCommunications in Medicine (DICOM) format. In other embodiments, thedigital processing system 516 may generate other standard ornon-standard digital image formats.

Additionally, the digital processing system 516 may transmit diagnosticimage files such as DICOM files to the treatment planning system 530over a data link 560. In one embodiment, the data link 560 may be adirect link, a local area network (LAN) link, a wide area network (WAN)link such as the Internet, or another type of data link. Furthermore,the information transferred between the diagnostic imaging system 510and the treatment planning system 530 may be either pulled or pushedacross the data link 560, such as in a remote diagnosis or treatmentplanning configuration. For example, a user may utilize embodiments ofthe present invention to remotely diagnose or plan treatments despitethe existence of a physical separation between the system user and thepatient.

The illustrated treatment planning system 530 includes a processingdevice 532, a system memory device 534, an electronic data storagedevice 536, a display device 538, and an input device 540. Theprocessing device 532, system memory 534, storage 536, display 538, andinput device 540 may be coupled together by one or more communicationchannel 542 such as a bus.

The processing device 532 receives and processes image data. Theprocessing device 532 also processes instructions and operations withinthe treatment planning system 530. In certain embodiments, theprocessing device 532 may include one or more general-purpose processors(e.g., a microprocessor), special purpose processors such as a digitalsignal processor (DSP), or other types of devices such as a controlleror field programmable gate array (FPGA).

In particular, the processing device 532 may be configured to executeinstructions for performing treatment operations discussed herein. Forexample, the processing device 532 may identify a non-linear path ofmovement of a target within a patient and develop a non-linear model ofthe non-linear path of movement. In another embodiment, the processingdevice 532 may develop the non-linear model based on a plurality ofposition points and a plurality of direction indicators. In anotherembodiment, the processing device 532 may generate a plurality ofcorrelation models and select one of the plurality of models to derive aposition of the target. Furthermore, the processing device 532 mayfacilitate other diagnosis, planning, and treatment operations relatedto the operations described herein.

In one embodiment, the system memory 534 may include random accessmemory (RAM) or other dynamic storage devices. As described above, thesystem memory 534 may be coupled to the processing device 532 by thecommunication channel 542. In one embodiment, the system memory 534stores information and instructions to be executed by the processingdevice 532. The system memory 534 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions by the processing device 532. In another embodiment, thesystem memory 534 also may include a read only memory (ROM) or otherstatic storage device for storing static information and instructionsfor the processing device 532.

In one embodiment, the storage 536 is representative of one or more massstorage devices (e.g., a magnetic disk drive, tape drive, optical diskdrive, etc.) to store information and instructions. The storage 536and/or the system memory 534 also may be referred to as machine readablemedia. In a specific embodiment, the storage 536 may store instructionsto perform the modeling operations discussed herein. For example, thestorage 536 may store instructions to acquire and store data points,acquire and store images, identify non-linear paths, develop linearand/or non-linear correlation models, select a correlation model from aplurality of models, and so forth. In another embodiment, the storage536 may include one or more databases.

In one embodiment, the display 538 may be a cathode ray tube (CRT)display, a liquid crystal display (LCD), or another type of displaydevice. The display 538 displays information (e.g., a two-dimensional orthree-dimensional representation of the VOI) to a user. The input device540 may include one or more user interface devices such as a keyboard,mouse, trackball, or similar device. The input device(s) 540 may also beused to communicate directional information, to select commands for theprocessing device 532, to control cursor movements on the display 538,and so forth.

Although one embodiment of the treatment planning system 530 isdescribed herein, the described treatment planning system 530 is onlyrepresentative of an exemplary treatment planning system 530. Otherembodiments of the treatment planning system 530 may have many differentconfigurations and architectures and may include fewer or morecomponents. For example, other embodiments may include multiple buses,such as a peripheral bus or a dedicated cache bus. Furthermore, thetreatment planning system 530 also may include Medical Image Review andImport Tool (MIRIT) to support DICOM import so that images can be fusedand targets delineated on different systems and then imported into thetreatment planning system 530 for planning and dose calculations. Inanother embodiment, the treatment planning system 530 also may includeexpanded image fusion capabilities that allow a user to plan treatmentsand view dose distributions on any one of various imaging modalitiessuch as MRI, CT, PET, and so forth. Furthermore, the treatment planningsystem 530 may include one or more features of convention treatmentplanning systems.

In one embodiment, the treatment planning system 530 may share adatabase on the storage 536 with the treatment delivery system 550 sothat the treatment delivery system 550 may access the database prior toor during treatment delivery. The treatment planning system 530 may belinked to treatment delivery system 550 via a data link 570, which maybe a direct link, a LAN link, or a WAN link, as discussed above withrespect to data link 560. Where LAN, WAN, or other distributedconnections are implemented, any of components of the treatment system500 may be in decentralized locations so that the individual systems510, 530, 550 may be physically remote from one other. Alternatively,some or all of the functional features of the diagnostic imaging system510, the treatment planning system 530, or the treatment delivery system550 may be integrated with each other within the treatment system 500.

The illustrated treatment delivery system 550 includes a radiationsource 552, an imaging system 554, a digital processing system 556, anda treatment couch 558. The radiation source 552, imaging system 554,digital processing system 556, and treatment couch 558 may be coupled toone another via one or more communication channel 560. One example of atreatment delivery system 550 is shown and described in more detail withreference to FIG. 25.

In one embodiment, the radiation source 552 is a therapeutic or surgicalradiation source 552 to administer a prescribed radiation dose to atarget volume in conformance with a treatment plan. For example, thetarget volume may be an internal organ, a tumor, a region. Forconvenience, reference herein to the target volume or a target refers toany whole or partial organ, tumor, region, or other delineated volumethat is the subject of a treatment plan.

In one embodiment, the imaging system 554 of the treatment deliverysystem 550 captures intra-treatment images of a patient volume,including the target volume, for registration or correlation with thediagnostic images described above in order to position the patient withrespect to the radiation source. Similar to the diagnostic imagingsystem 510, the imaging system 554 of the treatment delivery system 550may include one or more sources and one or more detectors.

The treatment delivery system 550 also may include a digital processingsystem 556 to control the radiation source 552, the imaging system 554,and a treatment couch 558, which is representative of any patientsupport device. The digital processing system 556 may include one ormore general-purpose processors (e.g., a microprocessor), specialpurpose processors such as a digital signal processor (DSP), or otherdevices such as a controller or field programmable gate array (FPGA).Additionally, the digital processing system 556 may include othercomponents (not shown) such as memory, storage devices, networkadapters, and the like.

FIG. 25 is a schematic block diagram illustrating one embodiment of atreatment delivery system 550. The depicted treatment delivery system550 includes a radiation source 552, in the form of a linear accelerator(LINAC) 20, and a treatment couch 558, as described above. The treatmentdelivery system 550 also includes multiple imaging x-ray sources 575 anddetectors 580. The two x-ray sources 575 may be nominally aligned toproject imaging x-ray beams through a patient from at least twodifferent angular positions (e.g., separated by 90 degrees, 45 degrees,etc.) and aimed through the patient on the treatment couch 558 towardthe corresponding detectors 580. In another embodiment, a single largeimager may be used to be illuminated by each x-ray imaging source 575.Alternatively, other quantities and configurations of imaging sources575 and detectors 580 may be used. In one embodiment, the treatmentdelivery system 550 may be an image-guided, robotic-based radiationtreatment system (e.g., for performing radiosurgery) such as theCYBERKNIFE® system developed by Accuray Incorporated of California.

In the illustrated embodiment, the LINAC 20 is mounted on a robotic arm590. The robotic arm 590 may have multiple (e.g., 5 or more) degrees offreedom in order to properly position the LINAC 20 to irradiate a targetsuch as a pathological anatomy with a beam delivered from many angles inan operating volume around the patient. The treatment implemented withthe treatment delivery system 550 may involve beam paths with a singleisocenter (point of convergence), multiple isocenters, or without anyspecific isocenters (i.e., the beams need only intersect with thepathological target volume and do not necessarily converge on a singlepoint, or isocenter, within the target). Furthermore, the treatment maybe delivered in either a single session (mono-fraction) or in a smallnumber of sessions (hypo-fractionation) as determined during treatmentplanning. In one embodiment, the treatment delivery system 550 deliversradiation beams according to the treatment plan without fixing thepatient to a rigid, external frame to register the intra-operativeposition of the target volume with the position of the target volumeduring the pre-operative treatment planning phase.

As described above, the digital processing system 556 may implementalgorithms to register images obtained from the imaging system 554 withpre-operative treatment planning images obtained from the diagnosticimaging system 510 in order to align the patient on the treatment couch558 within the treatment delivery system 550. Additionally, these imagesmay be used to precisely position the radiation source 552 with respectto the target volume or target.

In one embodiment, the treatment couch 558 may be coupled to secondrobotic arm (not shown) having multiple degrees of freedom. For example,the second arm may have five rotational degrees of freedom and onesubstantially vertical, linear degree of freedom. Alternatively, thesecond arm may have six rotational degrees of freedom and onesubstantially vertical, linear degree of freedom. In another embodiment,the second arm may have at least four rotational degrees of freedom.Additionally, the second arm may be vertically mounted to a column orwall, or horizontally mounted to pedestal, floor, or ceiling.Alternatively, the treatment couch 558 may be a component of anothermechanism, such as the AXUM® treatment couch developed by AccurayIncorporated of California. In another embodiment, the treatment couch558 may be another type of treatment table, including a conventionaltreatment table.

Although one exemplary treatment delivery system 550 is described above,the treatment delivery system 550 may be another type of treatmentdelivery system. For example, the treatment delivery system 550 may be agantry based (isocentric) intensity modulated radiotherapy (IMRT)system, in which a radiation source 552 (e.g., a LINAC 20) is mounted onthe gantry in such a way that it rotates in a plane corresponding to anaxial slice of the patient. Radiation may be delivered from severalpositions on the circular plane of rotation. In another embodiment, thetreatment delivery system 550 may be a stereotactic frame system such asthe GAMMAKNIFE®, available from Elekta of Sweden.

FIG. 26 illustrates a three-dimensional perspective view of a radiationtreatment process. In particular, FIG. 26 depicts several radiationbeams directed at a target 10. In one embodiment, the target 10 may berepresentative of an internal organ, a region within a patient, apathological anatomy such as a tumor or lesion, or another type ofobject or area of a patient. The target 10 also may be referred toherein as a target region, a target volume, and so forth, but each ofthese references is understood to refer generally to the target 10,unless indicated otherwise.

The illustrated radiation treatment process includes a first radiationbeam 12, a second radiation beam 14, a third radiation beam 16, and afourth radiation beam 18. Although four radiation beams 12-18 are shown,other embodiments may include fewer or more radiation beams. Forconvenience, reference to one radiation beam 12 is representative of allof the radiation beams 12-18, unless indicated otherwise. Additionally,the treatment sequence for application of the radiation beams 12-18 maybe independent of their respective ordinal designations.

In one embodiment, the four radiation beams 12 are representative ofbeam delivery based on conformal planning, in which the radiation beams12 pass through or terminate at various points within target region 10.In conformal planning, some radiation beams 12 may or may not intersector converge at a common point in three-dimensional space. In otherwords, the radiation beams 12 may be non-isocentric in that they do notnecessarily converge on a single point, or isocenter. However, theradiation beams 12 may wholly or partially intersect at the target 10with one or more other radiation beams 12.

In another embodiment, the intensity of each radiation beam 12 may bedetermined by a beam weight that may be set by an operator or bytreatment planning software. The individual beam weights may depend, atleast in part, on the total prescribed radiation dose to be delivered totarget 10, as well as the cumulative radiation dose delivered by some orall of the radiation beams 12. For example, if a total prescribed doseof 3500 cGy is set for the target 10, the treatment planning softwaremay automatically predetermine the beam weights for each radiation beam12 in order to balance conformality and homogeneity to achieve thatprescribed dose. Conformality is the degree to which the radiation dosematches (conforms to) the shape and extent of the target 10 (e.g.,tumor) in order to avoid damage to critical adjacent structures.Homogeneity is the uniformity of the radiation dose over the volume ofthe target 10. The homogeneity may be characterized by a dose volumehistogram (DVH), which ideally may be a rectangular function in which100 percent of the prescribed dose would be over the volume of thetarget 10 and would be zero everywhere else.

The method described above offers many advantages, compared to currentlyknow methods that are restricted to linear models. A first advantage, ofcourse, is that this method does not assume linear movement, and appliesto any general movement for internal organs. This method thereforerealistically takes account of the actual motion of the internal organs.A second advantage is that this method can successfully identify thecorrelation model for organs that traverse along different paths duringinspiration and expiration, respectively.

In sum, a method and system are presented for identifying curvilinearinternal organ movements. The above described method and system candetect and identify whether a patient's internal organ moves (duringrespiration of the patient) along different paths during the inspirationand the expiration phases of the respiration, respectively. Theabove-described method allows a correlation model to be constructed thatcan accurately estimate the position of an internal organ that eitherundergoes curvilinear movement, or moves along different paths duringthe inspiration and the expiration phases of the respiration, or both.Any other types of non-linear motion of an organ can also be fittedusing curvilinear models as described above, by choosing appropriateparameter fitting models, e.g. higher-order polynomial fitting methods,as just one example. The method described above permits the targeting ofinternal lesions and/or tumors that move with respiration (or otherpatient motion), for purpose of delivering therapeutic radiation to thelesions and tumors.

While the method and system above have been described in conjunctionwith respiratory motion of the patient, other embodiments may trackasymmetric, curvilinear (or otherwise nonlinear) motion of the internalorgans that occur during any other type of motion of the patient, e.g.heartbeat.

While the correlation method and system have been particularly shown anddescribed with reference to specific embodiments, it should beunderstood by those skilled in the art that various changes in form anddetail may be made therein without departing from the spirit and scopeof the invention.

It should be noted that the methods and apparatus described herein arenot limited to use only with medical diagnostic imaging and treatment.In alternative embodiments, the methods and apparatus herein may be usedin applications outside of the medical technology field, such asindustrial imaging and non-destructive testing of materials (e.g., motorblocks in the automotive industry, airframes in the aviation industry,welds in the construction industry and drill cores in the petroleumindustry) and seismic surveying. In such applications, for example,“treatment” may refer generally to the effectuation of an operationcontrolled by treatment planning software, such as the application of abeam (e.g., radiation, acoustic, etc.).

Although the operations of the method(s) herein are shown and describedin a particular order, the order of the operations of each method may bealtered so that certain operations may be performed in an inverse orderor so that certain operation may be performed, at least in part,concurrently with other operations. In another embodiment, instructionsor sub-operations of distinct operations may be in an intermittentand/or alternating manner.

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

1. A computer-implemented method, comprising: receiving a plurality ofdata points representative of a corresponding plurality of positionsover time of an external marker associated with respiration of apatient; receiving a plurality of images of an internal target of thepatient, the plurality of images corresponding in time to the pluralityof data points; developing, using a processing device, a non-linearcorrelation model of a non-linear path of movement of the target basedon the plurality of data points and the plurality of images to correlatea plurality of positions of the internal target with the plurality ofpositions of the external marker, wherein developing the non-linearcorrelation model comprises: generating an inspiration approximationassociated with the non-linear path of movement of the target over aninspiration interval of the non-linear path; generating an expirationapproximation associated with the non-linear path of movement of thetarget over an expiration interval of the non-linear path; andgenerating one or more matching approximations to link the inspirationapproximation and the expiration approximation.
 2. The method of claim1, wherein the non-linear correlation model comprises a polynomialapproximation of the non-linear path of movement of the target.
 3. Themethod of claim 1, wherein the non-linear correlation model comprises amulti-linear model having a plurality of linear approximations connectedat a non-zero angle.
 4. The method of claim 1, wherein developing thenon-linear correlation model comprises generating one or derivativesfrom the plurality of data points, the one or more derivativescomprising one or more directional indicators.
 5. The method of claim 1,wherein the inspiration approximation and the expiration approximationare derived from at least partially overlapping data sets.
 6. The methodof claim 1, further comprising deriving a target position of the targetbased on the non-linear correlation model.
 7. The method of claim 6,further comprising: sending a position signal associated with the targetposition to a beam generator controller; and controlling a beamgenerator to direct a beam at the target position.
 8. The method ofclaim 1, wherein the non-linear correlation model comprises atwo-dimensional model or a three-dimensional model.
 9. An apparatus,comprising: a data storage device to store a plurality of data pointsrepresentative of a corresponding plurality of positions over time of anexternal marker, the data storage device to store a plurality of imagesof an internal target of the patient, the plurality of imagescorresponding in time to the plurality of data points; and a processingdevice coupled to the data storage device, the processing deviceconfigured to: develop a non-linear correlation model of a non-linearpath of movement of the target based on the plurality of data points andthe plurality of images to correlate a plurality of positions of theinternal target with the plurality of positions of the external marker,wherein developing the non-linear correlation model comprises: generatean inspiration approximation associated with the non-linear path ofmovement of the target over an inspiration interval of the non-linearpath; generate an expiration approximation associated with thenon-linear path of movement of the target over an expiration interval ofthe non-linear path; and generate one or more matching approximations tolink the inspiration approximation and the expiration approximation. 10.The apparatus of claim 9, wherein the non-linear correlation modelcomprises a polynomial approximation of the non-linear path of movementof the target.
 11. The apparatus of claim 9, wherein the non-linearcorrelation model comprises a multi-linear model having a plurality oflinear approximations connected at a non-zero angle.
 12. The apparatusof claim 9, wherein developing the non-linear correlation modelcomprises generating one or derivatives from the plurality of datapoints, the one or more derivatives comprising one or more directionalindicators.
 13. The apparatus of claim 9, wherein the inspirationapproximation and the expiration approximation are derived from at leastpartially overlapping data sets.
 14. The apparatus of claim 9, furthercomprising: a beam generator controller operatively coupled withprocessing device, wherein the processing device is further configuredto derive a target position of the target based on the non-linearcorrelation model and sending a position signal associated with thetarget position to the beam generator controller; a beam generatoroperatively coupled with the beam generator controller to direct a beamat the target position.
 15. A non-transitory machine readable storagemedium having instructions thereon, which when executed by a processingdevice, cause the processing device to perform one or more operationscomprising: receiving a plurality of data points representative of acorresponding plurality of positions over time of an external markerassociated with respiration of a patient; receiving a plurality ofimages of an internal target of the patient, the plurality of imagescorresponding in time to the plurality of data points; developing, usinga processing device, a non-linear correlation model of a non-linear pathof movement of the target based on the plurality of data points and theplurality of images to correlate a plurality of positions of theinternal target with the plurality of positions of the external marker,wherein developing the non-linear correlation model comprises:generating an inspiration approximation associated with the non-linearpath of movement of the target over an inspiration interval of thenon-linear path; generating an expiration approximation associated withthe non-linear path of movement of the target over an expirationinterval of the non-linear path; and generating one or more matchingapproximations to link the inspiration approximation and the expirationapproximation.
 16. The non-transitory machine readable storage medium ofclaim 15, wherein the non-linear correlation model comprises apolynomial approximation of the non-linear path of movement of thetarget.
 17. The non-transitory machine readable storage medium of claim15, wherein the non-linear correlation model comprises a multi-linearmodel having a plurality of linear approximations connected at anon-zero angle.
 18. The non-transitory machine readable storage mediumof claim 15, wherein developing the non-linear correlation modelcomprises generating one or derivatives from the plurality of datapoints, the one or more derivatives comprising one or more directionalindicators.
 19. The non-transitory machine readable storage medium ofclaim 15, wherein the inspiration approximation and the expirationapproximation are derived from at least partially overlapping data sets.20. The non-transitory machine readable storage medium of claim 15,wherein the one or more operations further comprise deriving a targetposition of the target based on the non-linear correlation model. 21.The non-transitory machine readable storage medium of claim 20, whereinthe one or more operations further comprise: sending a position signalassociated with the target position to a beam generator controller; andcontrolling a beam generator to direct a beam at the target position.